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23rd International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2020

作者： Bayat, Amirhossein Sekuboyina, Anjany Paetzold, Johannes C. Payer, Christian Stern, Darko Urschler, Martin Kirschke, Jan S. Menze, Bjoern H. Department of Informatics Technical University of Munich Munich Germany Department of Neuroradiology Klinikum rechts der Isar Munich Germany Institute of Computer Graphics and Vision Graz University of Technology Graz Austria School of Computer Science University of Auckland Auckland New Zealand

ISBN: (纸本)9783030597245

The treatment of degenerative spinal disorders requires an understanding of the individual spinal anatomy and curvature in 3D. An upright spinal pose (i.e. standing) under natural weight bearing is crucial for such bio-mechanical analysis. 3D volumetric imaging modalities (e.g. CT and MRI) are performed in patients lying down. On the other hand, radiographs are captured in an upright pose, but result in 2D projections. This work aims to integrate the two realms, i.e. it combines the upright spinal curvature from radiographs with the 3D vertebral shape from CT imaging for synthesizing an upright 3D model of spine, loaded naturally. Specifically, we propose a novel neural network architecture working vertebra-wise, termed TransVert, which takes orthogonal 2D radiographs and infers the spines 3D posture. We validate our architecture on digitally reconstructed radiographs, achieving a 3D reconstruction Dice of 95.52 %, indicating an almost perfect 2D-to-3D domain translation. Deploying our model on clinical radiographs, we successfully synthesise full-3D, upright, patient-specific spine models for the first time. © 2020, Springer Nature Switzerland AG.

关键词： Magnetic resonance imaging

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Performance Analysis of a Cloud Computing System using Queuing Model with Correlated Task Reneging 5

Performance Analysis of a Cloud Computing System using Queui...

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5th International Scientific Conference on Information, Control, and Communication Technologies, ICCT 2021

作者： Kumar, Rakesh Soodan, Bhavneet Singh Kuaban, Godlove Suila Czekalski, Piotr Sharma, Sapana School of Mathematics Shri Mata Vaishno Devi University Jammu and Kashmir Katra182320 India Department of Mathematics and Statistics Namibia University of Science and Technology Private Bag 13388 Windhoek Namibia 13 Jackson Kaujeua Street Windhoek Namibia Institute of Theoretical and Applied Informatics Polish Academy of Sciences Baltycka 5 Gliwice44 - 100 Poland Department of Computer Graphics. Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Poland Department of Mathematics Cluster University of Jammu Jammu and Kashmir India

Queuing theory has been extensively used in the modelling and performance analysis of cloud computing systems. The phenomenon of the task (or request) reneging, that is, the dropping of requests from the request queue often occur in cloud computing systems, and it is important to consider it when developing performance evaluations models for cloud computing infrastructures. Majority of studies in the performance evaluation of cloud computing data centres with the use of queuing theory do not consider the fact that the tasks could be removed from queue without being serviced. The removal of tasks from the queue could be due to the user impatience, execution deadline expiration, security reasons, or as an active queue management strategy. The reneging could be correlated in nature, that is, if a request is dropped (or reneged) at any time epoch, and then there is a probability that a request may or may not be dropped at the next time epoch. This kind of dropping (or reneging) of requests is referred to as correlated request reneging. In this paper we have modelled a cloud computing infrastructure with correlated request reneging using queuing theory. An M/M/1/N queuing model with correlated reneging has been used to study the performance analysis of the load balancing server of a cloud computing system. The steady-state as well as the transient performance analyses have been carried out. Important measures of performance like a vera ge queue size, average delay, probability of task blocking, and the probability of no waiting in the queue are studied. Finally, some comparisons are performed which describe the effect of correlated task reneging over simple exponential reneging. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Cloud computing

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Development of a Realistic Crowd Simulation Environment for Fine-grained Validation of People Tracking Methods

arXiv

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arXiv 2023年

作者： Foszner, Pawel Szczęsna, Agnieszka Ciampi, Luca Messina, Nicola Cygan, Adam Bizon, Bartosz Cogiel, Michal Golba, Dominik Macioszek, Elżbieta Staniszewski, Michal Department of Computer Graphics Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Gliwice Poland Institute of Information Science and Technologies National Research Council Pisa Italy QSystems.pro sp. z o.o. Mochnackiego 34 Bytom41-907 Poland Blees sp. z o.o. Zygmunta Starego 24a/10 Gliwice44-100 Poland Department of Transport Systems Traffic Engineering and Logistics Faculty of Transport and Aviation Engineering Silesian University of Technology Katowice Poland

Generally, crowd datasets can be collected or generated from real or synthetic sources. Real data is generated by using infrastructure-based sensors (such as static cameras or other sensors). The use of simulation tools can significantly reduce the time required to generate scenario-specific crowd datasets, facilitate data-driven research, and next build functional machine learning models. The main goal of this work was to develop an extension of crowd simulation (named CrowdSim2) and prove its usability in the application of people-tracking algorithms. The simulator is developed using the very popular Unity 3D engine with particular emphasis on the aspects of realism in the environment, weather conditions, traffic, and the movement and models of individual agents. Finally, three methods of tracking were used to validate generated dataset: IOU-Tracker, Deep-Sort, and Deep-TAMA. © 2023, CC BY.

关键词： Deep learning

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CrowdSim2: an Open Synthetic Benchmark for Object Detectors

arXiv

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arXiv 2023年

Data scarcity has become one of the main obstacles to developing supervised models based on Artificial Intelligence in computer Vision. Indeed, Deep Learning-based models systematically struggle when applied in new scenarios never seen during training and may not be adequately tested in non-ordinary yet crucial real-world situations. This paper presents and publicly releases CrowdSim2, a new synthetic collection of images suitable for people and vehicle detection gathered from a simulator based on the Unity graphical engine. It consists of thousands of images gathered from various synthetic scenarios resembling the real world, where we varied some factors of interest, such as the weather conditions and the number of objects in the scenes. The labels are automatically collected and consist of bounding boxes that precisely localize objects belonging to the two object classes, leaving out humans from the annotation pipeline. We exploited this new benchmark as a testing ground for some state-of-the-art detectors, showing that our simulated scenarios can be a valuable tool for measuring their performances in a controlled environment. © 2023, CC BY.

关键词： Object detection

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Integrating Artificial Intelligence and Machine Learning for Competitive Advantage in Developing Business Ecosystems

Integrating Artificial Intelligence and Machine Learning for...

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Trends in Quantum Computing and Emerging Business Technologies (TQCEBT), International Conference on

作者： Srinivas D C. Vijai Krishan Bansal Shaunak Pal Sumeet Gupta Punamkumar Hinge School of Business SR University Warangal Telangana Department of Commerce and Business Administration Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Lovely Professional University School of Computer Science and Engineering Vellore Institute of Technology Vellore Tamil Nadu India Professor and Cluster Head –Global Economics and Finance Cluster School of Business University of Petroleum and Energy Studies Management Indus Business School Pune Maharashtra

ISBN: (数字)9798350384277

ISBN: (纸本)9798350384284

The previous few years have witnessed significant shifts in people's aspirations and lives as a result of technology improvements. When discussing the advancements in technology over the past twenty years, we must first discuss the pre-Internet era before moving on to the Internet *** that, systems that are service-oriented replaced those of the Internet age. After then, we entered the era of social applications and left behind service-oriented systems. The social application era was eventually superseded by the age of artificial intelligence and the Internet of *** theory, subsequent development, and integration of intelligent behavior akin to that of humans in machines is known as artificial intelligence, or AI. A branch of AI called machine learning has important uses in many different fields of *** recent years, there has been a substantial advancement in the business model framework for artificial intelligence and machine learning. Nonetheless, there is one area that needs more investigation and understanding even though it is crucial for academics, policymakers, and aspiring managers to know: how companies are evolving their AI business model frameworks to achieve ongoing value *** and experience on AI business model frameworks have consequences for machine learning (ML) in this investigation. In order to create and adapt their company model and maintain value generation, executives can use this tool to reproduce and categorize significant difficulties. We want to investigate the theoretical underpinnings of the Business Model Framework in this study, which uses Consumer Business Analytics to transform the business market ecology.

关键词： Adaptation models Analytical models Quantum computing Biological system modeling Machine learning Transforms Companies

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Developing a computer-Aided Diagnostic System for Breast Cancer Ultrasound Imaging

Developing a Computer-Aided Diagnostic System for Breast Can...

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Data science and Engineering in Healthcare, Medicine and Biology, IEEE EMBS Special Topic Conference on

作者： Radwa Taha Shereen Afifi Mohamed A. Abd-ElGhany Mohammed A.-M Salem Master Candidate in the Faculty of Media Engineering and Technology German University in Cairo New Cairo Egypt Assistant Professor in Computer Science Engineering Faculty of Media Engineering and Technology German University in Cairo New Cairo Egypt Professor in Electrical Engineering Faculty of Information Engineering and Technology German University in Cairo New Cairo Egypt Integrated Electronics Systems Lab TU Darmstadt Germany Digital Media Engineering Faculty of Media Engineering and Technology German University in Cairo New Cairo Egypt

In this research we aim to develop a computer-aided diagnostic system (CAD) for breast ultrasound imaging to enhance the early detection of breast cancer. CAD tools aid physicians in the diagnostic process leading to early detection and treatment of breast cancer. The proposed CAD tool comprises three stages, the first is enhancement of breast ultrasound imaging by applying custom segmentation deep learning architectures [U-Net, SK-U-Net, RDA-U-Net] to enhance the isolation of masses in Ultrasound images, then generation of new ultrasound images by employing Generative Adversarial Networks [DAGAN, DCGAN] to overcome data limitation in available data-sets, and the last stage is the detection and classification of breast cancer masses by a CNN model. The enhancement stage shows promising results with accuracy of 98 % overall accuracy, 92 % overall precision, and 85 % overall *** Relevance: Breast cancer is one of the deadly diseases in the world. The earlier the detection the higher the chances of recovery. Thus a CAD tool that facilitates the screening process of ultrasound images will aid physicians in the early detection and diagnosis of the disease.

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Automatic diagnosis of disc herniation based on DenseNet fusion model 8

Automatic diagnosis of disc herniation based on DenseNet fus...

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8th International Conference on Digital Home, ICDH 2020

作者： Wang, Zimin Qin, Junyan Huang, Jinrong Wang, Yumeng Li, Ji Guilin University of Electronic Technology Guangxi Colleges and Universities Key Lab. of Intelligent Processing of Computer Image and Graphics Guilin541004 China Guilin Huigu Institute of Artificial Intelligence Industrial Technology Guilin541004 China Guilin University of Electronic Technology School of Computer Science and Information Security Guilin541004 China

ISBN: (纸本)9781728192345

Spine MRI images generally have the characteristics of low contrast and much noise. Because the variable shape of the spine edge, the traditional spine image segmentation method requires a lot of preprocessing and can not perform image segmentation automatically, which is inefficient and cannot compliant the clinical work requirements. So as to solve the above problems, this paper use deep learning algorithms to identify the spine images, and proposed a disc herniation recognition method for spinal MRI images which based on DenseNet fusion model. Use DenseNet and Inception-Resnet-V2 model fusion to get more image features and improve the image recognition accuracy. Experiments prove that this method can be used to identify the herniation of spinal intervertebral disc with high accuracy and real-time. © 2020 IEEE.

关键词： Image segmentation

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AI Framework for Keys Instruction and Music Notation

AI Framework for Keys Instruction and Music Notation

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Communication, Computing and Energy Efficient Technologies (I3CEET), International Conference on

作者： Krishnaraj Rao N S Smitha GL P. S. Ramesh Nandhini T J P. Nagaveni A Balaji Assistant Professor Department of Information Science and Engineering Nitte (Deemed to be University) NMAM Institute of Technology (NMAMIT) Nitte Karnataka India Department of CSE Saveetha School of Engineering Saveetha Institute of Medical and Technical Science (SIMATS) Chennai India Department of CSE Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India Department of Computer Science and Engineering Saveetha School of Enginnering Saveetha Institute of Medical and Technical Science – SIMATS Saveetha University Chennai India Department of Electrical and Electronics Engineering Karpagam Academy of Higher Education Coimbatore Department of IT Panimalar Engineering College

ISBN: (数字)9798331541583

ISBN: (纸本)9798331541590

The study of artificial intelligence focuses on the principles that guide all human intellectual endeavors. The integration of several academic disciplines, including as computer science, philosophy, politics, linguistics, systems engineering, neurophysiology, and behavioral science, has allowed it to grow. This article will describe how to build an intelligent machine, consisting of both hardware and software, that can replicate and exceed the abilities of the human brain in order to handle complicated issues that need specialized knowledge. It will specifically examine how to more effectively use artificial intelligence support systems into piano instruction. An Introduction, an oral language and music-assisted education system based on the ARM and SA algorithm, is proposed by this study in order to ascertain the implementation principle guiding the functioning of the piano automation recording system. It also delves deeply into the theory and operation of automated music recording systems, compiling and evaluating data that these systems generate via internal databases. is an effort to improve music education via the use of artificial intelligence support systems. The results of the experiment show that the training system for the automated notation algorithms for playing the piano and songs has a 56.81 and fast growing machine learning auxiliary technology utilization rate. Thus, we may infer that using an AI support system is necessary while teaching performance piano and automated notation.

关键词： Training Machine learning algorithms Education Music Machine learning Software Recording Multiple signal classification Telecommunication computing Artificial intelligence

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A Development of Control0020Techniques for Design of OM Based Robots Using Fuzzy Logic

A Development of Control0020Techniques for Design of OM Base...

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Advance Computing and Innovative Technologies in Engineering (ICACITE), International Conference on

作者： Alagappan Vikrant Sharma Ramy Riad Al-Fatlawy Sherin Eliyas Tareq Hafdhi Abdtawfeeq Israa Jabbar Falah Hassan Abdullah Ali Saad Alwan Department of computer applications Hindustan institute of technology and science Department of CSE Graphic Era Hill University Adjunct Professor Graphic Era Deemed to be University Dehradun Uttarakhand Department of computers Techniques engineering College of technical engineering The Islamic university Najaf Iraq Al-Farahidi university Baghdad Iraq Law Department Al-kunooze University College Basra Iraq Department of Forensic Sciences College of Science National University of Science and Technology DhiQar Iraq Business Administration Department Al kut Kut University College wasit Iraq

ISBN: (数字)9798350360165

ISBN: (纸本)9798350360172

In this study, a fuzzy controller design specifically for omnidirectional robots is presented. We explore mobile robot dynamics and kinematics, providing a basis for our suggested fuzzy control technique. Our method targets the football robots created in our research lab and deals with the mobile robot’s position and rotational angle control. Our created control algorithms demonstrate effective navigation skills for these football robots through extensive simulation testing. The positive outcomes attested to the efficacy of the controllers we suggested. We also do an analysis of the performance characteristics of our proposed control systems, which shows significant improvements in regulation times for modifying orientation angles and attaining new locations. In the future, we want to investigate how Z number-based fuzzy techniques might be applied to improve control system performance even *** is potential to improve our control tactics and achieve even higher efficiency in robotic navigation tasks by utilising the Z number system.

关键词： Navigation Heuristic algorithms Kinematics Control systems Regulation Mobile robots Task analysis

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Sclera Segmentation and Joint Recognition Benchmarking Competition: SSRBC 2023

Sclera Segmentation and Joint Recognition Benchmarking Compe...

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IEEE International Joint Conference on Biometrics (IJCB)

作者： Abhijit Das Saurabh Atreya Aritra Mukherjee Matej Vitek Haiqing Li Caiyong Wang Guangzhe Zhao Fadi Boutros Patrick Siebke Jan Niklas Kolf Naser Damer Sun Ye Lu Hexin Fan Aobo You Sheng Sabari Nathan R. Suganya R. S. Rampriya Geetanjali Sharma P Priyanka Aditya Nigam Peter Peer Umapada Pal Vitomir Štruc Birla Institute of Technology and Science Pilani (BITS Pilani) India University of Ljubljana (UL Slovenia) Beijing University of Civil Engineering and Architecture (BUCEA) China Fraunhofer Institute for Computer Graphics Research IGD (IGD) Germany TU Darmstadt Germany Jilin University (JLU China) Cougar Inc (Japan) Vellore Institure of Technology Vellore (VIT India) Indian Institute of Technology Mandi (IIT-MD India) Indian Statistical Institute Kolkata (ISI India)

This paper presents the summary of the Sclera Segmentation and Joint Recognition Benchmarking Competition (SSRBC 2023) held in conjunction with IEEE International Joint Conference on Biometrics (IJCB 2023). Different from the previous editions of the competition, SSRBC 2023 not only explored the performance of the latest and most advanced sclera segmentation models, but also studied the impact of segmentation quality on recognition performance. Five groups took part in SSRBC 2023 and submitted a total of six segmentation models and one recognition technique for scoring. The submitted solutions included a wide variety of conceptually diverse deep-learning models and were rigorously tested on three publicly available datasets, i.e., MASD, SBVPI and MOBIUS. Most of the segmentation models achieved encouraging segmentation and recognition performance. Most importantly, we observed that better segmentation results always translate into better verification performance.

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